Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

Pan, Qiaosheng; Zhang, Chi; Wei, Xinyuan; Wan, Anhua; Wei, Zhe

doi:10.1016/j.epsr.2023.109427

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total number of faults is 46. Therefore, the results-46 faults and 64 normal-are basically consistent with sample data and the results from Reference [22] (47 faults and 63 normal results were obtained), which proves that the evaluation methods proposed in this paper are effective and more precise than the method proposed in Reference [22].…”

Section: Example Simulation and Analysis For The Power Cable Status E...supporting

confidence: 83%

“…Based on 110 groups of sample data, including 46 groups of faulty data and 64 groups of normal cable data, we can obtain the sample score results to evaluate the effectiveness of this method. The sample score results for the method proposed in this paper and those from Reference [22] are shown in Figure 4. For the method proposed in this paper, Figure 4 shows that there are 36 faults with scores ranging from 0 to 20 (severe faults in the cable); 6 faults with scores ranging from 21 to 40 (abnormal status for the cable); and 4 faults with scores ranging from 41 to 60 (mild abnormal status for the cable).…”

Section: Example Simulation and Analysis For The Power Cable Status E...mentioning

confidence: 99%

See 1 more Smart Citation

RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules

Wang,

et al. 2023

Processes

View full text Add to dashboard Cite

The partial discharge (PD) analysis method has remained one of the key technologies for cable condition assessment to diagnose cable operation state. In this paper, two power cable status evaluation methods are proposed based on electrical tree growth and data association rules, respectively. First, in the field of molecular thermodynamics, based on the law of energy conservation of electrical tree growth, this paper studies the relationship between PD signals in cable insulation and the length of the electrical tree. By using the length of electrical tree growth to calculate the probability of PD failure of cables, a cable state assessment method based on electrical tree growth is proposed. Second, for power cable status evaluation, the strong concealment characteristics of cables result in a long power supply recovery time and a serious threat to power supply reliability. Therefore, a group of comprehensive cable status parameters are set by analyzing the status parameters based on data association rules. The comprehensive state of power cables is divided into four elements, and the weight coefficients of cable states are calculated based on data association rule methods. Finally, the simulation results verify the effectiveness and accuracy for the two different power cable status evaluation methods.

show abstract

Section: Example Simulation and Analysis For The Power Cable Status E...supporting

confidence: 83%

Section: Example Simulation and Analysis For The Power Cable Status E...mentioning

confidence: 99%

RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules

Wang,

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…However, the potential of decision tree models is limited by problems such as poor stability, sensitivity to data distribution, propensity to fit, and unreliable generalization performance. With the development of artificial intelligence technology, the XGBoost algorithm has shown good performance on classification problems [14]. It integrates multiple weak learners via combinatorial learning to build a strong learner to eliminate these limitations and improve its performance.…”

Section: Xgboost Algorithmmentioning

confidence: 99%

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Zhao,

Jia,

et al. 2024

Processes

View full text Add to dashboard Cite

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

show abstract

Experimental analysis of combustion characteristics of corn starch dust clouds under the action of unilateral obstacles and machine learning modeling based on PSO-XGBoost

Zhang,

Cao,

et al. 2024

Advanced Powder Technology

View full text Add to dashboard Cite

Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

Cited by 5 publications

References 10 publications

RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules

RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Experimental analysis of combustion characteristics of corn starch dust clouds under the action of unilateral obstacles and machine learning modeling based on PSO-XGBoost

Contact Info

Product

Resources

About